Models for risk prediction are widely used in clinical practice to risk stratify and assign treatment strategies. Risk reclassification, or cross-stratification into clinical risk categories, can be used to assess the potential impact of new models on treatment decisions for individual patients. Measures based on this reclassification, including those assessing both calibration and discrimination, will be described and compared to continuous or category-free measures. Advantages of each will be discussed, and performance in practical situations will be examined. Results of simulations estimating the type I error and power for these statistics when adding a new marker to an established or reference model will be presented for a number of scenarios, as well as the impact of the number and type of categories. The type I error appears reasonable in most settings. The relative power of the measures varies depending on the model assumptions. These tools provide unique but complementary information.